Aerial image segmentation for flood risk analysis

Neil M. Robertson, Tak Chan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper presents a technique for image segmentation. We demonstrate its efficacy for classsifying high-resolution aerial images. The application is peak water flow estimation in a river catchment in the city of Zurich and the data covers a large rural and urban setting. The output of the segmentation process is used as input to a hydrological model. We introduce a combined, probabilistic, segmentation approach based on colour (the LAB colour space is used), texture (using entropy) and image features (gradients). Classification rates for natural land surfaces and man-made structures are up to 90% and 85% respectively. When the automatic segmentation result is compared to the official land use data and reclassified for use in GIS we achieve an overall classification accuracy of 70%. This new classification is tested on the WetSpa hydrological model and the resulting flow estimate compares favourably with that computed from hand-classified land use data. ©2009 IEEE.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
Pages597-600
Number of pages4
DOIs
Publication statusPublished - 2009
Event16th IEEE International Conference on Image Processing 2009 - Cairo, Egypt
Duration: 7 Nov 200912 Nov 2009

Conference

Conference16th IEEE International Conference on Image Processing 2009
Abbreviated titleICIP 2009
CountryEgypt
CityCairo
Period7/11/0912/11/09

Keywords

  • Colour
  • Hydrological mapping
  • Segmentation
  • Texture

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